---
license: gemma
language:
- en
base_model:
- google/gemma-3-12b-it
base_model_relation: finetune
pipeline_tag: text-generation
tags:
- conversational-ai
- conversational
- collaboration
- education
- educational
- vanta-research
- collaborative-ai
- chat
- reasoning
- problem-solving
- educational
- research
- gemma3
- warm
- cognitive
- reasoning
- LLM
- text-generation
- learning
- research-assistance
library_name: transformers
---
---

<div align="center">

![vanta_trimmed](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F686c460ba3fc457ad14ab6f8%2FhcGtMtCIizEZG_OuCvfac.png)
  
  <h1>VANTA Research</h1>
    
  <p><strong>Independent AI research lab building safe, resilient language models optimized for human-AI collaboration</strong></p>
  
  <p>
    <a href="https://vantaresearch.xyz"><img src="https://img.shields.io/badge/Website-vantaresearch.xyz-black" alt="Website"/></a>
    <a href="https://merch.vantaresearch.xyz"><img src="https://img.shields.io/badge/Merch-merch.vantaresearch.xyz-sage" alt="Merch"/></a>
    <a href="https://x.com/vanta_research"><img src="https://img.shields.io/badge/@vanta_research-1DA1F2?logo=x" alt="X"/></a>
    <a href="https://github.com/vanta-research"><img src="https://img.shields.io/badge/GitHub-vanta--research-181717?logo=github" alt="GitHub"/></a>
  </p>
</div>

---

# Atom V1 Preview 12B

Atom V1 Preview 12B is a fine-tuned conversational AI model based on Google's Gemma 3 12B Instruct architecture. This model is designed to function as a collaborative thought partner, specializing in exploratory dialogue, brainstorming, research assistance, and technical problem-solving while maintaining an approachable and engaging conversational style. 

This 12B iteration of the Atom persona is the third release in Project Atom from VANTA Research, and is also our largest model to date.  

## Model Details

**Model Type:** Multimodal Transformer (Text + Vision)  
**Base Model:** google/gemma-3-12b-it  
**Training Method:** Low-Rank Adaptation (LoRA) fine-tuning  
**License:** Gemma Terms of Use  
**Developed By:** VANTA Research  
**Language:** English  

### Architecture

- **Parameters:** 12 billion
- **Hidden Size:** 3840
- **Attention Heads:** 16 (8 key-value heads)
- **Hidden Layers:** 48
- **Context Window:** 131,072 tokens
- **Sliding Window:** 1,024 tokens
- **FFN Dimension:** 15,360
- **Vocabulary Size:** 262,208 tokens
- **Precision:** FP16

The model employs a hybrid attention pattern with sliding window attention and periodic full attention layers (every 6th layer) for efficient long-context processing.

## Training Methodology

Atom-v1-preview-12b was fine-tuned using parameter-efficient LoRA adapters targeting attention and feedforward components. The training data consists of curated conversational examples emphasizing:

- Collaborative exploration and brainstorming
- Research synthesis and question formulation
- Technical explanation at varying complexity levels
- Lateral thinking and creative problem-solving
- Empathetic and supportive dialogue patterns

Training was conducted over 258 steps with careful monitoring to preserve the base model's technical capabilities while introducing enhanced conversational characteristics.

## Intended Use

### Primary Applications

- **Collaborative Brainstorming:** Generating diverse ideas and building iteratively on user suggestions
- **Research Assistance:** Synthesizing information, identifying key arguments, and formulating research questions
- **Technical Explanation:** Simplifying complex concepts across difficulty levels (including ELI5)
- **Code Discussion:** Exploring implementation approaches, debugging strategies, and architectural decisions
- **Creative Problem-Solving:** Encouraging unconventional approaches and lateral thinking

### Out-of-Scope Use

This model is a research preview and should not be used for:
- High-stakes decision-making without human oversight
- Medical, legal, or financial advice
- Generation of harmful, biased, or misleading content
- Applications requiring guaranteed factual accuracy

## Usage

### Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "atom-v1-preview-12-hf",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("atom-v1-preview-12-hf")

messages = [
    {"role": "user", "content": "What's your approach to explaining quantum entanglement?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=512,
    temperature=0.8,
    top_p=0.9,
    top_k=40,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Recommended Sampling Parameters

- **Temperature:** 0.7-0.9 (higher for creative tasks)
- **Top-p:** 0.9
- **Top-k:** 40
- **Repetition Penalty:** 1.1
- **Max Context:** 8,192 tokens (longer contexts supported but may impact performance)

## Performance Characteristics

Based on systematic evaluation across conversational dimensions:

- **Collaborative Framing:** Strong "thought partner" identity with organic question flow
- **Enthusiasm Expression:** Consistent use of engaged language patterns without over-prescription
- **Metaphor Usage:** Effective across technical and creative contexts
- **Technical Competence:** Maintains depth while prioritizing accessibility
- **Adaptability:** Calibrates tone and complexity to conversational context

The model demonstrates 85-90% alignment with design specifications across diverse prompt types, including identity awareness, technical discussion, creative output, empathetic support, and philosophical reasoning.

## Limitations

- **Knowledge Cutoff:** Training data reflects information available through late 2024
- **Factual Accuracy:** May generate plausible-sounding but incorrect information
- **Quantization Impact:** 4-bit GGUF quantization trades model size for minor quality degradation
- **Context Processing:** Very long contexts (>32K tokens) may show attention degradation
- **Domain Specificity:** Strongest in general technical discussion; may lack depth in highly specialized domains
- **Bias:** Inherits biases from base model and training data despite mitigation efforts

## Ethical Considerations

This model is designed to support exploration and learning, not to replace human judgment. Users should:

- Verify factual claims against authoritative sources
- Apply critical thinking to generated suggestions
- Recognize the model's limitations in high-stakes scenarios
- Be mindful of potential biases in outputs
- Use responsibly in accordance with applicable laws and regulations

## Citation

```bibtex
@misc{atom-v1-preview-12,
  title={Atom-v1-preview-12: A Collaborative Thought Partner},
  author={VANTA Research},
  year={2025},
  howpublished={https://huggingface.co/vanta-research/atom-v1-preview-12b}
}
```

## Acknowledgments

Built on Google's Gemma 3 12B Instruct architecture. Training infrastructure supported by Hugging Face Spaces, Transformers, PEFT, and llama.cpp quantization tools.
Atom V1 12B was trained on NVIDIA's L40S GPU

## Contact

For questions, issues, or collaboration inquiries, please open an issue in the repository or contact the development team directly.
- **Email:** hello@vantaresearch.xyz